Radar sensor system for vehicles

ABSTRACT

A radio detection and ranging (RADAR) sensor system for vehicles, such as autonomous vehicles, includes a first RADAR sensor configured to provide first RADAR data descriptive of an environment of a vehicle having a first antenna configured to output a first RADAR beam having a first azimuthal component over a first angular range and a second RADAR sensor configured to provide second RADAR data descriptive of the environment of the vehicle, the second RADAR sensor having a second antenna configured to output a second RADAR beam having a second azimuthal component that is narrower than the first azimuthal component of the first RADAR beam, wherein the second RADAR sensor is configured to sweep the second RADAR beam over a second angular range closer to a rear of the vehicle than a front of the vehicle to obtain the second RADAR data.

BACKGROUND

An autonomous platform can process data to perceive an environmentthrough which the autonomous platform can travel. For example, anautonomous vehicle can perceive its environment using a variety ofsensors and identify objects around the autonomous vehicle. Theautonomous vehicle can identify an appropriate path through theperceived surrounding environment and navigate along the path withminimal or no human input.

SUMMARY

An autonomous vehicle, such as an autonomous vehicle towing a trailer(e.g., an autonomous truck) can navigate through the use of a RADARsystem. The RADAR system may be, for example, a MIMO RADAR system. Theazimuthal components of a RADAR beam output by the MIMO RADAR system canreflect off the sidewalls of the trailer, providing false detections ofobjects, multipath ghosts, interference, and other complications.

According to example aspects of the present disclosure, however, asecond RADAR beam, such as a “pencil beam” directed primarily in onedirection, can supplement detections from the first RADAR beam inregions on the autonomous vehicle that are sensitive to multipathinterference, such as in an angular range bordering the trailer.Additionally, the first RADAR beam may not be directed in at least aportion of the angular range covered by the second RADAR beam. Becausethe second RADAR beam is narrower, less energy from azimuthal componentsof the second beam reflects off the sidewalls of the trailer, therebyreducing multipath interference. Additionally and/or alternatively,because the second RADAR beam is more concentrated than the first RADARbeam in a singular direction (e.g., with less energy on azimuthalextremes), the second RADAR beam can provide an increased range ofdetection of objects, including smaller objects (e.g., motorcycles,pedestrians, etc.) in the angular range covered by the second beam. Thiscan help detect objects within “blind spots” of the autonomous truck.

Example aspects of the present disclosure provide for a number oftechnical effects and benefits. As one example, aspects of the presentdisclosure can provide for improved detection of objects in anenvironment proximate to an autonomous platform. For instance, the useof a second RADAR sensor having a second antenna configured to output asecond RADAR beam having a second (e.g., narrow) azimuthal component canreduce the effects of multipath interference from portions of theautonomous platform, thereby increasing the accuracy of sensor data fromthe RADAR system. Furthermore, the reduced multipath interference canlead to improved understanding of the environment of the autonomousvehicle, which can provide for more efficient and/or accurate motionplanning for the autonomous vehicle. For instance, a motion plan of anautonomous vehicle may not have to account for a falsely-detected objectcaused by multipath interference, which can provide for the motion planto take a more efficient path than if it were to have to “avoid” thefalsely-detected object. Additionally and/or alternatively, exampleaspects of the present disclosure can reduce fuel consumption as well asimprove motion planning performance by autonomous vehicles by improvingefficiency of handling certain scenarios such as lane changes, mergingfrom a road shoulder, avoiding static objects and/or lane closures, andso on. Furthermore, example aspects of the present disclosure canimprove the functionality of computer-related technologies by reducingcomputing resource usage lost to multipath interference, such asprocessing (e.g., filtering) techniques, data point storage associatedwith false multipath points, and so on.

For example, in an aspect, the present disclosure provides a radiodetection and ranging (RADAR) sensor system for vehicles, such asautonomous vehicles. The RADAR sensor system includes a first RADARsensor configured to generate first RADAR data descriptive of anenvironment of a vehicle having a first antenna configured to output afirst RADAR beam having a first azimuthal component over a first angularrange and a second RADAR sensor configured to provide second RADAR datadescriptive of the environment of the vehicle. The second RADAR sensorincludes a second antenna configured to output a second RADAR beamhaving a second azimuthal component that is narrower than the firstazimuthal component of the first RADAR beam. The second RADAR sensor isconfigured to sweep the second RADAR beam over a second angular rangecloser to a rear of the vehicle than a front of the vehicle to obtainthe second RADAR data.

In some implementations, the first antenna includes a MIMO antenna.

In some implementations, the second antenna includes a beam steeringantenna that is directed to a trailer coupled to the vehicle.

In some implementations, the vehicle can be or can include an autonomoustruck

In some implementations, the first angular range is configured to coverthe front of the vehicle.

In some implementations, the second angular range is configured to beproximate to a trailer coupled to the vehicle.

In some implementations, a transmit pattern of the second antennaincludes power focused in a second azimuthal component with less than 1degree azimuthal span and greater than 0.1 degree azimuthal span.

In some implementations, a transmit pattern of the first antennaincludes power radiated over a first azimuthal component with more than120 degree azimuthal span and less than 180 degree azimuthal span.

In some implementations, the second angular range includes less thanthirty degrees and greater than zero degrees.

In another example aspect, the present disclosure provides an autonomousvehicle control system including: (a) one or more processors; and (b)one or more non-transitory, computer-readable media storing instructionsthat are executable to cause the one or more processors to performoperations. The operations include obtaining first RADAR data includingone or more first data points associated with a first angular range ofan environment of an autonomous vehicle from a first RADAR sensor. Thefirst RADAR sensor includes a first antenna configured to output a firstRADAR beam having a first azimuthal component. The operations includeobtaining second RADAR data including one or more second data pointsassociated with a second angular range of the environment of theautonomous vehicle from a second RADAR sensor. Obtaining the secondRADAR data includes sweeping the second RADAR beam over a second angularrange. The second RADAR sensor includes a second antenna configured tooutput a second RADAR beam having a second azimuthal component that isnarrower than the first azimuthal component of the first RADAR beam. Thesecond RADAR sensor is configured to sweep the second RADAR beam overthe second angular range which is closer to a rear of the autonomousvehicle than a front of the autonomous vehicle to obtain the secondRADAR data. The operations include detecting one or more objects in theenvironment of the autonomous vehicle based on the first RADAR data andthe second RADAR data.

In some implementations, detecting one or more objects in theenvironment of the autonomous vehicle based on the first RADAR data andthe second RADAR data includes providing the first RADAR data and thesecond RADAR data to a perception system of the autonomous vehicle.

In some implementations, providing the first RADAR data and the secondRADAR data to a perception system includes providing the first RADARdata and the second RADAR data to a sensor data fusion module configuredto fuse at least the first RADAR data and the second RADAR data togenerate fused RADAR data including a point-cloud representation of theenvironment of the autonomous vehicle.

In some implementations, sweeping the second RADAR beam over the secondangular range includes: broadcasting the second RADAR beam in a firstangular direction of the second angular range; obtaining a first portionof the second RADAR data associated with the first angular directionwith the second RADAR beam broadcasted in the first angular direction;broadcasting the second RADAR beam in a second angular direction of thesecond angular range; and obtaining a second portion of the second RADARdata associated with the second angular direction with the second RADARbeam broadcasted in the second angular direction.

In some implementations, the operations further include: determining,based on the one or more objects in the environment of the autonomousvehicle, a motion trajectory for navigating the autonomous vehicle; andcontrolling the autonomous vehicle based on the motion trajectory tonavigate the autonomous vehicle through the environment.

In some implementations, the autonomous vehicle comprises an autonomoustruck.

In some implementations, the second RADAR sensor is positioned on a rearportion of the autonomous truck.

In some implementations, the autonomous truck includes a sensor bedpositioned above a cabin of the autonomous truck, wherein the secondRADAR sensor is positioned within the sensor bed.

In another example aspect the present disclosure provides an autonomousvehicle. The autonomous vehicle includes a first RADAR sensor includinga first antenna configured to output a first RADAR beam over a firstangular range, the first RADAR beam having a first azimuthal component.The autonomous vehicle includes a second RADAR sensor including a secondantenna configured to output a second RADAR beam having a secondazimuthal component that is narrower than the first azimuthal componentof the first RADAR beam. The second RADAR sensor is configured to sweepthe second RADAR beam over a second angular range which is closer to arear of the autonomous vehicle than a front of the autonomous vehicle.The autonomous vehicle includes an autonomous vehicle control system.The autonomous vehicle control system includes one or more processorsand one or more non-transitory, computer-readable media storinginstructions that are executable to cause the one or more processors toperform operations. The operations include obtaining first RADAR dataincluding one or more first data points associated with the firstangular range of an environment of the autonomous vehicle from the firstRADAR sensor. The operations include obtaining second RADAR dataincluding one or more second data points associated with the secondangular range of the environment of the autonomous vehicle from secondRADAR sensor. Obtaining the second RADAR data includes sweeping thesecond RADAR beam over the second angular range. The operations includedetecting one or more objects in the environment of the autonomousvehicle based on the first RADAR data and the second RADAR data.

In some implementations, the operations include: determining, based onthe one or more objects in the environment of the autonomous vehicle, amotion trajectory for navigating the autonomous vehicle; and controllingthe autonomous vehicle based on the motion trajectory to navigate theautonomous vehicle through the environment.

In some implementations, sweeping the second RADAR beam over the secondangular range includes: broadcasting the second RADAR beam in a firstangular direction of the second angular range; obtaining a first portionof the second RADAR data associated with the first angular directionwith the second RADAR beam broadcasted in the first angular direction;broadcasting the second RADAR beam in a second angular direction of thesecond angular range; and obtaining a second portion of the second RADARdata associated with the second angular direction with the second RADARbeam broadcasted in the second angular direction.

In some implementations, detecting one or more objects in theenvironment of the autonomous vehicle based on the first RADAR data andthe second RADAR data includes providing the first RADAR data and thesecond RADAR data to a perception system of the autonomous vehicle.Providing the first RADAR data and the second RADAR data to theperception system includes providing the first RADAR data and the secondRADAR data to a sensor data fusion module configured to fuse at leastthe first RADAR data and the second RADAR data to generate fused RADARdata including a point-cloud representation of the environment of theautonomous vehicle.

Other example aspects of the present disclosure are directed to othersystems, methods, vehicles, apparatuses, tangible non-transitorycomputer-readable media, and devices for operation of a RADAR sensorsystem, or systems including a RADAR sensor system, as well asprocessing and utilizing the associated sensor data for system control.

These and other features, aspects and advantages of variousimplementations of the present disclosure will become better understoodwith reference to the following description and appended claims. Theaccompanying drawings, which are incorporated in and constitute a partof this specification, illustrate embodiments of the present disclosureand, together with the description, serve to explain the relatedprinciples.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example operating scenario according tosome implementations of the present disclosure.

FIG. 2 is a block diagram of example autonomy system(s) for anautonomous platform according to some implementations of the presentdisclosure.

FIGS. 3A-3C are an example operating environment for an autonomousplatform according to some implementations of the present disclosure.

FIG. 4 is an example autonomous truck according to some implementationsof the present disclosure.

FIGS. 5A-5B are an example operating environment for an autonomousplatform according to some implementations of the present disclosure.

FIGS. 6A-6B are example transmit patterns for RADAR systems according tosome implementations of the present disclosure.

FIGS. 7A-7B are example antennas for RADAR systems illustrating beamsteering according to some implementations of the present disclosure.

FIGS. 8A-8B are example angular ranges covered by RADAR systems in anenvironment of an autonomous platform according to some implementationsof the present disclosure.

FIGS. 9A-9D are example uses cases for applications of autonomousvehicle control systems according to some implementations of the presentdisclosure.

FIG. 10 is a flowchart of a method according to some implementations ofthe present disclosure.

FIG. 11 is a block diagram of an example computing system according tosome implementations of the present disclosure.

DETAILED DESCRIPTION

The following describes the technology of this disclosure within thecontext of an autonomous vehicle for example purposes only. As describedherein, the technology described herein is not limited to an autonomousvehicle and can be implemented for or within other autonomous platformsand other computing systems. As used herein, “about” in conjunction witha stated numerical value is intended to refer to within 20 percent ofthe stated numerical value, except where otherwise indicated.

With reference to FIGS. 1-11 , example implementations of the presentdisclosure are discussed in further detail. FIG. 1 is a block diagram ofan example operational scenario, according to some implementations ofthe present disclosure. In the example operational scenario, anenvironment 100 contains an autonomous platform 110 and a number ofobjects, including first actor 120, second actor 130, and third actor140. In the example operational scenario, the autonomous platform 110can move through the environment 100 and interact with the object(s)that are located within the environment 100 (e.g., first actor 120,second actor 130, third actor 140, etc.). The autonomous platform 110can optionally be configured to communicate with remote system(s) 160through network(s) 170.

The environment 100 may be or include an indoor environment (e.g.,within one or more facilities, etc.) or an outdoor environment. Anindoor environment, for example, may be an environment enclosed by astructure such as a building (e.g., a service depot, maintenancelocation, manufacturing facility, etc.). An outdoor environment, forexample, may be one or more areas in the outside world such as, forexample, one or more rural areas (e.g., with one or more rural travelways, etc.), one or more urban areas (e.g., with one or more city travelways, highways, etc.), one or more suburban areas (e.g., with one ormore suburban travel ways, etc.), or other outdoor environments.

The autonomous platform 110 may be any type of platform configured tooperate within the environment 100. For example, the autonomous platform110 may be a vehicle configured to autonomously perceive and operatewithin the environment 100. The vehicles may be a ground-basedautonomous vehicle such as, for example, an autonomous car, truck, van,etc. The autonomous platform 110 may be an autonomous vehicle that cancontrol, be connected to, or be otherwise associated with implements,attachments, and/or accessories for transporting people or cargo. Thiscan include, for example, an autonomous tractor optionally coupled to acargo trailer. Additionally or alternatively, the autonomous platform110 may be any other type of vehicle such as one or more aerialvehicles, water-based vehicles, space-based vehicles, other ground-basedvehicles, etc.

The autonomous platform 110 may be configured to communicate with theremote system(s) 160. For instance, the remote system(s) 160 cancommunicate with the autonomous platform 110 for assistance (e.g.,navigation assistance, situation response assistance, etc.), control(e.g., fleet management, remote operation, etc.), maintenance (e.g.,updates, monitoring, etc.), or other local or remote tasks. In someimplementations, the remote system(s) 160 can provide data indicatingtasks that the autonomous platform 110 should perform. For example, asfurther described herein, the remote system(s) 160 can provide dataindicating that the autonomous platform 110 is to perform a trip/servicesuch as a user transportation trip/service, delivery trip/service (e.g.,for cargo, freight, items), etc.

The autonomous platform 110 can communicate with the remote system(s)160 using the network(s) 170. The network(s) 170 can facilitate thetransmission of signals (e.g., electronic signals, etc.) or data (e.g.,data from a computing device, etc.) and can include any combination ofvarious wired (e.g., twisted pair cable, etc.) or wireless communicationmechanisms (e.g., cellular, wireless, satellite, microwave, radiofrequency, etc.) or any desired network topology (or topologies). Forexample, the network(s) 170 can include a local area network (e.g.,intranet, etc.), a wide area network (e.g., the Internet, etc.), awireless LAN network (e.g., through Wi-Fi, etc.), a cellular network, aSATCOM network, a VHF network, a HF network, a WiMAX based network, orany other suitable communications network (or combination thereof) fortransmitting data to or from the autonomous platform 110.

As shown for example in FIG. 1 , the environment 100 can include one ormore objects. The object(s) may be objects not in motion or notpredicted to move (“static objects”) or object(s) in motion or predictedto be in motion (“dynamic objects” or “actors”). In someimplementations, the environment 100 can include any number of actor(s)such as, for example, one or more pedestrians, animals, vehicles, etc.The actor(s) can move within the environment according to one or moreactor trajectories. For instance, the first actor 120 can move along anyone of the first actor trajectories 122A-C, the second actor 130 canmove along any one of the second actor trajectories 132, the third actor140 can move along any one of the third actor trajectories 142, etc.

As further described herein, the autonomous platform 110 can utilize itsautonomy system(s) to detect these actors (and their movement) and planits motion to navigate through the environment 100 according to one ormore platform trajectories 112A-C. The autonomous platform 110 caninclude onboard computing system(s) 180. The onboard computing system(s)180 can include one or more processors and one or more memory devices.The one or more memory devices can store instructions executable by theone or more processors to cause the one or more processors to performoperations or functions associated with the autonomous platform 110,including implementing its autonomy system(s).

FIG. 2 is a block diagram of an example autonomy system 200 for anautonomous platform, according to some implementations of the presentdisclosure. In some implementations, the autonomy system 200 can beimplemented by a computing system of the autonomous platform (e.g., theonboard computing system(s) 180 of the autonomous platform 110). Theautonomy system 200 can operate to obtain inputs from sensor(s) 202 orother input devices. In some implementations, the autonomy system 200can additionally obtain platform data 208 (e.g., map data 210) fromlocal or remote storage. The autonomy system 200 can generate controloutputs for controlling the autonomous platform (e.g., through platformcontrol devices 212, etc.) based on sensor data 204, map data 210, orother data. The autonomy system 200 may include different subsystems forperforming various autonomy operations. The subsystems may include alocalization system 230, a perception system 240, a planning system 250,and a control system 260. The localization system 230 can determine thelocation of the autonomous platform within its environment; theperception system 240 can detect, classify, and track objects and actorsin the environment; the planning system 250 can determine a trajectoryfor the autonomous platform; and the control system 260 can translatethe trajectory into vehicle controls for controlling the autonomousplatform. The autonomy system 200 can be implemented by one or moreonboard computing system(s). The subsystems can include one or moreprocessors and one or more memory devices. The one or more memorydevices can store instructions executable by the one or more processorsto cause the one or more processors to perform operations or functionsassociated with the subsystems. The computing resources of the autonomysystem 200 can be shared among its subsystems, or a subsystem can have aset of dedicated computing resources.

In some implementations, the autonomy system 200 can be implemented foror by an autonomous vehicle (e.g., a ground-based autonomous vehicle).The autonomy system 200 can perform various processing techniques oninputs (e.g., the sensor data 204, the map data 210) to perceive andunderstand the vehicle's surrounding environment and generate anappropriate set of control outputs to implement a vehicle motion plan(e.g., including one or more trajectories) for traversing the vehicle'ssurrounding environment (e.g., environment 100 of FIG. 1 , etc.). Insome implementations, an autonomous vehicle implementing the autonomysystem 200 can drive, navigate, operate, etc. with minimal or nointeraction from a human operator (e.g., driver, pilot, etc.).

In some implementations, the autonomous platform can be configured tooperate in a plurality of operating modes. For instance, the autonomousplatform can be configured to operate in a fully autonomous (e.g.,self-driving, etc.) operating mode in which the autonomous platform iscontrollable without user input (e.g., can drive and navigate with noinput from a human operator present in the autonomous vehicle or remotefrom the autonomous vehicle, etc.). The autonomous platform can operatein a driver assistance (e.g., advanced driver assistance or ADAS)operating mode in which the autonomous platform can operate with someinput from a human operator present in the autonomous platform (or ahuman operator that is remote from the autonomous platform). In someimplementations, the autonomous platform can enter into a manualoperating mode in which the autonomous platform is fully controllable bya human operator (e.g., human driver, etc.) and can be prohibited ordisabled (e.g., temporary, permanently, etc.) from performing autonomousnavigation (e.g., autonomous driving, etc.). The autonomous platform canbe configured to operate in other modes such as, for example, park orsleep modes (e.g., for use between tasks such as waiting to provide atrip/service, recharging, etc.). In some implementations, the autonomousplatform can implement vehicle operating assistance technology (e.g.,collision mitigation system, power assist steering, etc.), for example,to help assist the human operator of the autonomous platform (e.g.,while in a manual mode, etc.).

The autonomy system 200 can be located onboard (e.g., on or within) anautonomous platform and can be configured to operate the autonomousplatform in various environments. The environment may be a real-worldenvironment or a simulated environment. In some implementations, one ormore simulation computing devices can simulate one or more of: thesensors 202, the sensor data 204, communication interface(s) 206, theplatform data 208, or the platform control devices 212 for simulatingoperation of the autonomy system 200.

In some implementations, the autonomy system 200 can communicate withone or more networks or other systems with the communicationinterface(s) 206. The communication interface(s) 206 can include anysuitable components for interfacing with one or more network(s) (e.g.,the network(s) 170 of FIG. 1 , etc.), including, for example,transmitters, receivers, ports, controllers, antennas, or other suitablecomponents that can help facilitate communication. In someimplementations, the communication interface(s) 206 can include aplurality of components (e.g., antennas, transmitters, or receivers,etc.) that allow it to implement and utilize various communicationtechniques (e.g., multiple-input, multiple-output (MIMO) technology,etc.).

In some implementations, the autonomy system 200 can use thecommunication interface(s) 206 to communicate with one or more computingdevices that are remote from the autonomous platform (e.g., the remotesystem(s) 160) over one or more network(s) (e.g., the network(s) 170).For instance, in some examples, one or more inputs, data, orfunctionalities of the autonomy system 200 can be supplemented orsubstituted by a remote system communicating over the communicationinterface(s) 206. For instance, in some implementations, the map data210 can be downloaded over a network to a remote system using thecommunication interface(s) 206. In some examples, one or more of thelocalization system 230, the perception system 240, the planning system250, or the control system 260 can be updated, influenced, nudged,communicated with, etc. by a remote system for assistance, maintenance,situational response override, management, etc.

The sensor(s) 202 can be located onboard the autonomous platform. Insome implementations, the sensor(s) 202 can include one or more types ofsensor(s). For instance, one or more sensors can include image capturingdevice(s) (e.g., visible spectrum cameras, infrared cameras, etc.).Additionally or alternatively, the sensor(s) 202 can include one or moredepth capturing device(s). For example, the sensor(s) 202 can includeone or more Light Detection and Ranging (LIDAR) sensor(s) or RadioDetection and Ranging (RADAR) sensor(s). The sensor(s) 202 can beconfigured to generate point data descriptive of at least a portion of athree-hundred-and-sixty-degree view of the surrounding environment. Thepoint data can be point cloud data (e.g., three-dimensional LIDAR pointcloud data, RADAR point cloud data). In some implementations, one ormore of the sensor(s) 202 for capturing depth information can be fixedto a rotational device in order to rotate the sensor(s) 202 about anaxis. The sensor(s) 202 can be rotated about the axis while capturingdata in interval sector packets descriptive of different portions of athree-hundred-and-sixty-degree view of a surrounding environment of theautonomous platform. In some implementations, one or more of thesensor(s) 202 for capturing depth information can be solid state.

The sensor(s) 202 can be configured to capture the sensor data 204indicating or otherwise being associated with at least a portion of theenvironment of the autonomous platform. The sensor data 204 can includeimage data (e.g., 2D camera data, video data, etc.), RADAR data, LIDARdata (e.g., 3D point cloud data, etc.), audio data, or other types ofdata. In some implementations, the autonomy system 200 can obtain inputfrom additional types of sensors, such as inertial measurement units(IMUs), altimeters, inclinometers, odometry devices, location orpositioning devices (e.g., GPS, compass), wheel encoders, or other typesof sensors. In some implementations, the autonomy system 200 can obtainsensor data 204 associated with particular component(s) or system(s) ofan autonomous platform. This sensor data 204 can indicate, for example,wheel speed, component temperatures, steering angle, cargo or passengerstatus, etc. In some implementations, the autonomy system 200 can obtainsensor data 204 associated with ambient conditions, such asenvironmental or weather conditions. In some implementations, the sensordata 204 can include multi-modal sensor data. The multi-modal sensordata can be obtained by at least two different types of sensor(s) (e.g.,of the sensors 202) and can indicate static object(s) or actor(s) withinan environment of the autonomous platform. The multi-modal sensor datacan include at least two types of sensor data (e.g., camera and LIDARdata). In some implementations, the autonomous platform can utilize thesensor data 204 for sensors that are remote from (e.g., offboard) theautonomous platform. This can include for example, sensor data 204captured by a different autonomous platform.

Example aspects of the present disclosure provide for a RADAR systemhaving a second (e.g., narrow) azimuthal profile to identify objects inangular ranges of the environment of the autonomous platform that aresensitive to multipath interference, such as rear-facing angular rangesproximate to sidewalls of the trailer. For instance, a subset of theentire angular range of the autonomous platform may be covered by thisnarrower RADAR system, such as an angular range bounded on one side bythe sidewalls of the trailer. A narrow (e.g., less than 1 degreeazimuthal) beam from the RADAR system can be swept over the angularrange to identify objects in this angular range. In addition, becausethe beam-sweeping approach may be slower and/or have a higher hardwarecost than other antenna systems, a wider-band RADAR system can be usedto identify objects in the remaining angular range. Thus, the autonomousplatform can identify objects over a greater angular range whileavoiding multipath interference caused by some conventional RADARsystems.

The autonomy system 200 can obtain the map data 210 associated with anenvironment in which the autonomous platform was, is, or will belocated. The map data 210 can provide information about an environmentor a geographic area. For example, the map data 210 can provideinformation regarding the identity and location of different travel ways(e.g., roadways, etc.), travel way segments (e.g., road segments, etc.),buildings, or other items or objects (e.g., lampposts, crosswalks,curbs, etc.); the location and directions of boundaries or boundarymarkings (e.g., the location and direction of traffic lanes, parkinglanes, turning lanes, bicycle lanes, other lanes, etc.); traffic controldata (e.g., the location and instructions of signage, traffic lights,other traffic control devices, etc.); obstruction information (e.g.,temporary or permanent blockages, etc.); event data (e.g., roadclosures/traffic rule alterations due to parades, concerts, sportingevents, etc.); nominal vehicle path data (e.g., indicating an idealvehicle path such as along the center of a certain lane, etc.); or anyother map data that provides information that assists an autonomousplatform in understanding its surrounding environment and itsrelationship thereto. In some implementations, the map data 210 caninclude high-definition map information. Additionally or alternatively,the map data 210 can include sparse map data (e.g., lane graphs, etc.).In some implementations, the sensor data 204 can be fused with or usedto update the map data 210 in real-time.

The autonomy system 200 can include the localization system 230, whichcan provide an autonomous platform with an understanding of its locationand orientation in an environment. In some examples, the localizationsystem 230 can support one or more other subsystems of the autonomysystem 200, such as by providing a unified local reference frame forperforming, e.g., perception operations, planning operations, or controloperations.

In some implementations, the localization system 230 can determine acurrent position of the autonomous platform. A current position caninclude a global position (e.g., respecting a georeferenced anchor,etc.) or relative position (e.g., respecting objects in the environment,etc.). The localization system 230 can generally include or interfacewith any device or circuitry for analyzing a position or change inposition of an autonomous platform (e.g., autonomous ground-basedvehicle, etc.). For example, the localization system 230 can determineposition by using one or more of: inertial sensors (e.g., inertialmeasurement unit(s), etc.), a satellite positioning system, radioreceivers, networking devices (e.g., based on IP address, etc.),triangulation or proximity to network access points or other networkcomponents (e.g., cellular towers, Wi-Fi access points, etc.), or othersuitable techniques. The position of the autonomous platform can be usedby various subsystems of the autonomy system 200 or provided to a remotecomputing system (e.g., using the communication interface(s) 206).

In some implementations, the localization system 230 can registerrelative positions of elements of a surrounding environment of anautonomous platform with recorded positions in the map data 210. Forinstance, the localization system 230 can process the sensor data 204(e.g., LIDAR data, RADAR data, camera data, etc.) for aligning orotherwise registering to a map of the surrounding environment (e.g.,from the map data 210) to understand the autonomous platform's positionwithin that environment. Accordingly, in some implementations, theautonomous platform can identify its position within the surroundingenvironment (e.g., across six axes, etc.) based on a search over the mapdata 210. In some implementations, given an initial location, thelocalization system 230 can update the autonomous platform's locationwith incremental re-alignment based on recorded or estimated deviationsfrom the initial location. In some implementations, a position can beregistered directly within the map data 210.

In some implementations, the map data 210 can include a large volume ofdata subdivided into geographic tiles, such that a desired region of amap stored in the map data 210 can be reconstructed from one or moretiles. For instance, a plurality of tiles selected from the map data 210can be stitched together by the autonomy system 200 based on a positionobtained by the localization system 230 (e.g., a number of tilesselected in the vicinity of the position).

In some implementations, the localization system 230 can determinepositions (e.g., relative or absolute) of one or more attachments oraccessories for an autonomous platform. For instance, an autonomousplatform can be associated with a cargo platform, and the localizationsystem 230 can provide positions of one or more points on the cargoplatform. For example, a cargo platform can include a trailer or otherdevice towed or otherwise attached to or manipulated by an autonomousplatform, and the localization system 230 can provide for datadescribing the position (e.g., absolute, relative, etc.) of theautonomous platform as well as the cargo platform. Such information canbe obtained by the other autonomy systems to help operate the autonomousplatform.

The autonomy system 200 can include the perception system 240, which canallow an autonomous platform to detect, classify, and track objects andactors in its environment. Environmental features or objects perceivedwithin an environment can be those within the field of view of thesensor(s) 202 or predicted to be occluded from the sensor(s) 202. Thiscan include object(s) not in motion or not predicted to move (staticobjects) or object(s) in motion or predicted to be in motion (dynamicobjects/actors).

The perception system 240 can determine one or more states (e.g.,current or past state(s), etc.) of one or more objects that are within asurrounding environment of an autonomous platform. For example, state(s)can describe (e.g., for a given time, time period, etc.) an estimate ofan object's current or past location (also referred to as position);current or past speed/velocity; current or past acceleration; current orpast heading; current or past orientation; size/footprint (e.g., asrepresented by a bounding shape, object highlighting, etc.);classification (e.g., pedestrian class vs. vehicle class vs. bicycleclass, etc.); the uncertainties associated therewith; or other stateinformation. In some implementations, the perception system 240 candetermine the state(s) using one or more algorithms or machine-learnedmodels configured to identify/classify objects based on inputs from thesensor(s) 202. The perception system can use different modalities of thesensor data 204 to generate a representation of the environment to beprocessed by the one or more algorithms or machine-learned model. Insome implementations, state(s) for one or more identified orunidentified objects can be maintained and updated over time as theautonomous platform continues to perceive or interact with the objects(e.g., maneuver with or around, yield to, etc.). In this manner, theperception system 240 can provide an understanding about a current stateof an environment (e.g., including the objects therein, etc.) informedby a record of prior states of the environment (e.g., including movementhistories for the objects therein). Such information can be helpful asthe autonomous platform plans its motion through the environment.

The autonomy system 200 can include the planning system 250, which canbe configured to determine how the autonomous platform is to interactwith and move within its environment. The planning system 250 candetermine one or more motion plans for an autonomous platform. A motionplan can include one or more trajectories (e.g., motion trajectories)that indicate a path for an autonomous platform to follow. A trajectorycan be of a certain length or time range. The length or time range canbe defined by the computational planning horizon of the planning system250. A motion trajectory can be defined by one or more waypoints (withassociated coordinates). The waypoint(s) can be future location(s) forthe autonomous platform. The motion plans can be continuously generated,updated, and considered by the planning system 250.

The motion planning system 250 can determine a strategy for theautonomous platform. A strategy may be a set of discrete decisions(e.g., yield to actor, reverse yield to actor, merge, lane change) thatthe autonomous platform makes. The strategy may be selected from aplurality of potential strategies. The selected strategy may be a lowestcost strategy as determined by one or more cost functions. The costfunctions may, for example, evaluate the probability of a collision withanother actor or object.

The planning system 250 can determine a desired trajectory for executinga strategy. For instance, the planning system 250 can obtain one or moretrajectories for executing one or more strategies. The planning system250 can evaluate trajectories or strategies (e.g., with scores, costs,rewards, constraints, etc.) and rank them. For instance, the planningsystem 250 can use forecasting output(s) that indicate interactions(e.g., proximity, intersections, etc.) between trajectories for theautonomous platform and one or more objects to inform the evaluation ofcandidate trajectories or strategies for the autonomous platform. Insome implementations, the planning system 250 can utilize static cost(s)to evaluate trajectories for the autonomous platform (e.g., “avoid laneboundaries,” “minimize jerk,” etc.). Additionally or alternatively, theplanning system 250 can utilize dynamic cost(s) to evaluate thetrajectories or strategies for the autonomous platform based onforecasted outcomes for the current operational scenario (e.g.,forecasted trajectories or strategies leading to interactions betweenactors, forecasted trajectories or strategies leading to interactionsbetween actors and the autonomous platform, etc.). The planning system250 can rank trajectories based on one or more static costs, one or moredynamic costs, or a combination thereof. The planning system 250 canselect a motion plan (and a corresponding trajectory) based on a rankingof a plurality of candidate trajectories. In some implementations, theplanning system 250 can select a highest ranked candidate, or a highestranked feasible candidate. The planning system 250 can then validate theselected trajectory against one or more constraints before thetrajectory is executed by the autonomous platform.

To help with its motion planning decisions, the planning system 250 canbe configured to perform a forecasting function. The planning system 250can forecast future state(s) of the environment. This can includeforecasting the future state(s) of other actors in the environment. Insome implementations, the planning system 250 can forecast futurestate(s) based on current or past state(s) (e.g., as developed ormaintained by the perception system 240). In some implementations,future state(s) can be or include forecasted trajectories (e.g.,positions over time) of the objects in the environment, such as otheractors. In some implementations, one or more of the future state(s) caninclude one or more probabilities associated therewith (e.g., marginalprobabilities, conditional probabilities). For example, the one or moreprobabilities can include one or more probabilities conditioned on thestrategy or trajectory options available to the autonomous platform.Additionally or alternatively, the probabilities can includeprobabilities conditioned on trajectory options available to one or moreother actors.

In some implementations, the planning system 250 can perform interactiveforecasting. The planning system 250 can determine a motion plan for anautonomous platform with an understanding of how forecasted futurestates of the environment can be affected by execution of one or morecandidate motion plans. By way of example, with reference again to FIG.1 , the autonomous platform 110 can determine candidate motion planscorresponding to a set of platform trajectories 112A-C that respectivelycorrespond to the first actor trajectories 122A-C for the first actor120, trajectories 132 for the second actor 130, and trajectories 142 forthe third actor 140 (e.g., with respective trajectory correspondenceindicated with matching line styles). For instance, the autonomousplatform 110 (e.g., using its autonomy system 200) can forecast that aplatform trajectory 112A to more quickly move the autonomous platform110 into the area in front of the first actor 120 is likely associatedwith the first actor 120 decreasing forward speed and yielding morequickly to the autonomous platform 110 in accordance with first actortrajectory 122A. Additionally or alternatively, the autonomous platform110 can forecast that a platform trajectory 112B to gently move theautonomous platform 110 into the area in front of the first actor 120 islikely associated with the first actor 120 slightly decreasing speed andyielding slowly to the autonomous platform 110 in accordance with firstactor trajectory 122B. Additionally or alternatively, the autonomousplatform 110 can forecast that a platform trajectory 112C to remain in aparallel alignment with the first actor 120 is likely associated withthe first actor 120 not yielding any distance to the autonomous platform110 in accordance with first actor trajectory 122C. Based on comparisonof the forecasted scenarios to a set of desired outcomes (e.g., byscoring scenarios based on a cost or reward), the planning system 250can select a motion plan (and its associated trajectory) in view of theautonomous platform's interaction with the environment 100. In thismanner, for example, the autonomous platform 110 can interleave itsforecasting and motion planning functionality.

To implement selected motion plan(s), the autonomy system 200 caninclude a control system 260 (e.g., a vehicle control system).Generally, the control system 260 can provide an interface between theautonomy system 200 and the platform control devices 212 forimplementing the strategies and motion plan(s) generated by the planningsystem 250. For instance, the control system 260 can implement theselected motion plan/trajectory to control the autonomous platform'smotion through its environment by following the selected trajectory(e.g., the waypoints included therein). The control system 260 can, forexample, translate a motion plan into instructions for the appropriateplatform control devices 212 (e.g., acceleration control, brake control,steering control, etc.). By way of example, the control system 260 cantranslate a selected motion plan into instructions to adjust a steeringcomponent (e.g., a steering angle) by a certain number of degrees, applya certain magnitude of braking force, increase/decrease speed, etc. Insome implementations, the control system 260 can communicate with theplatform control devices 212 through communication channels including,for example, one or more data buses (e.g., controller area network(CAN), etc.), onboard diagnostics connectors (e.g., OBD-II, etc.), or acombination of wired or wireless communication links. The platformcontrol devices 212 can send or obtain data, messages, signals, etc. toor from the autonomy system 200 (or vice versa) through thecommunication channel(s).

The autonomy system 200 can receive, through communication interface(s)206, assistive signal(s) from remote assistance system 270. Remoteassistance system 270 can communicate with the autonomy system 200 overa network (e.g., as a remote system 160 over network 170). In someimplementations, the autonomy system 200 can initiate a communicationsession with the remote assistance system 270. For example, the autonomysystem 200 can initiate a session based on or in response to a trigger.In some implementations, the trigger may be an alert, an error signal, amap feature, a request, a location, a traffic condition, a roadcondition, etc.

After initiating the session, the autonomy system 200 can providecontext data to the remote assistance system 270. The context data mayinclude sensor data 204 and state data of the autonomous platform. Forexample, the context data may include a live camera feed from a cameraof the autonomous platform and the autonomous platform's current speed.An operator (e.g., human operator) of the remote assistance system 270can use the context data to select assistive signals. The assistivesignal(s) can provide values or adjustments for various operationalparameters or characteristics for the autonomy system 200. For instance,the assistive signal(s) can include way points (e.g., a path around anobstacle, lane change, etc.), velocity or acceleration profiles (e.g.,speed limits, etc.), relative motion instructions (e.g., convoyformation, etc.), operational characteristics (e.g., use of auxiliarysystems, reduced energy processing modes, etc.), or other signals toassist the autonomy system 200.

The autonomy system 200 can use the assistive signal(s) for input intoone or more autonomy subsystems for performing autonomy functions. Forinstance, the planning subsystem 250 can receive the assistive signal(s)as an input for generating a motion plan. For example, assistivesignal(s) can include constraints for generating a motion plan.Additionally or alternatively, assistive signal(s) can include cost orreward adjustments for influencing motion planning by the planningsubsystem 250. Additionally or alternatively, assistive signal(s) can beconsidered by the autonomy system 200 as suggestive inputs forconsideration in addition to other received data (e.g., sensor inputs,etc.).

The autonomy system 200 may be platform agnostic, and the control system260 can provide control instructions to platform control devices 212 fora variety of different platforms for autonomous movement (e.g., aplurality of different autonomous platforms fitted with autonomouscontrol systems). This can include a variety of different types ofautonomous vehicles (e.g., sedans, vans, SUVs, trucks, electricvehicles, combustion power vehicles, etc.) from a variety of differentmanufacturers/developers that operate in various different environmentsand, in some implementations, perform one or more vehicle services.

For example, with reference to FIG. 3A, an operational environment caninclude a dense environment 300. An autonomous platform can include anautonomous vehicle 350 controlled by the autonomy system 200. In someimplementations, the autonomous vehicle 350 can be configured formaneuverability in a dense environment, such as with a configuredwheelbase or other specifications. In some implementations, theautonomous vehicle 350 can be configured for transporting cargo orpassengers. In some implementations, the autonomous vehicle 350 can beconfigured to transport numerous passengers (e.g., a passenger van, ashuttle, a bus, etc.). In some implementations, the autonomous vehicle350 can be configured to transport cargo 352, such as large quantitiesof cargo (e.g., a truck, a box van, a step van, etc.) or smaller cargo(e.g., food, personal packages, etc.).

With reference to FIG. 3B, a selected overhead view 302 of the denseenvironment 300 is shown overlaid with an example trip/service between afirst location 304 and a second location 306. The example trip/servicecan be assigned, for example, to an autonomous vehicle 320 by a remotecomputing system. The autonomous vehicle 320 can be, for example, thesame type of vehicle as autonomous vehicle 350. The example trip/servicecan include transporting passengers or cargo between the first location304 and the second location 306. In some implementations, the exampletrip/service can include travel to or through one or more intermediatelocations, such as to onload or offload passengers or cargo. In someimplementations, the example trip/service can be prescheduled (e.g., forregular traversal, such as on a transportation schedule). In someimplementations, the example trip/service can be on-demand (e.g., asrequested by or for performing a taxi, rideshare, ride hailing, courier,delivery service, etc.).

With reference to FIG. 3C, a selected overhead view of open travel wayenvironment 330 is shown, including travel ways 332, an interchange 334,transfer hubs 336 and 338, access travel ways 340, and locations 342 and344. In some implementations, an autonomous vehicle (e.g., theautonomous vehicle 350) can be assigned an example trip/service totraverse the one or more travel ways 332 (optionally connected by theinterchange 334) to transport cargo between the transfer hub 336 and thetransfer hub 338. For instance, in some implementations, the exampletrip/service includes a cargo delivery/transport service, such as afreight delivery/transport service. The example trip/service can beassigned by a remote computing system. In some implementations, thetransfer hub 336 can be an origin point for cargo (e.g., a depot, awarehouse, a facility, etc.) and the transfer hub 338 can be adestination point for cargo (e.g., a retailer, etc.). However, in someimplementations, the transfer hub 336 can be an intermediate point alonga cargo item's ultimate journey between its respective origin and itsrespective destination. For instance, a cargo item's origin can besituated along the access travel ways 340 at the location 342. The cargoitem can accordingly be transported to the transfer hub 336 (e.g., by ahuman-driven vehicle, by the autonomous vehicle 350, etc.) for staging.At the transfer hub 336, various cargo items can be grouped or stagedfor longer distance transport over the travel ways 332.

In some implementations of an example trip/service, a group of stagedcargo items can be loaded onto an autonomous vehicle (e.g., theautonomous vehicle 350) for transport to one or more other transferhubs, such as the transfer hub 338. For instance, although not depicted,it is to be understood that the open travel way environment 330 caninclude more transfer hubs than the transfer hubs 336 and 338, and caninclude more travel ways 332 interconnected by more interchanges 334. Asimplified map is presented here for purposes of clarity only. In someimplementations, one or more cargo items transported to the transfer hub338 can be distributed to one or more local destinations (e.g., by ahuman-driven vehicle, by the autonomous vehicle 310, etc.), such asalong the access travel ways 340 to the location 344. In someimplementations, the example trip/service can be prescheduled (e.g., forregular traversal, such as on a transportation schedule). In someimplementations, the example trip/service can be on-demand (e.g., asrequested by or for performing a chartered passenger transport orfreight delivery service).

Autonomous platforms can understand the environment proximate to theautonomous platform through one or more sensors, such as RADAR systems.For instance, autonomous platforms can use radiofrequency signalsemitted by an antenna to determine the presence of objects in theenvironment through analysis of data captured through the RADAR system.Many RADAR antennas emit radiation in an azimuthal profile representedby a transmit pattern. In some cases, portions of the autonomousplatform may interfere with radiofrequency (RF) signals emitted by theRADAR systems, which can lead to false detection of objects in theenvironment.

According to example aspects of the present disclosure, an autonomousplatform can include one or more RADAR systems that include antennasthat emit radiation in a narrow azimuthal profile, such as a so-called“pencil band” antenna system. The transmit patterns of these systems canhave energy primarily directed towards a boresight of the antenna, withless energy at the azimuthal extremes of the transmit pattern. Becausethese narrow-band RADAR systems emit radiation in a narrow azimuthalprofile, they can be less sensitive to interference caused by energy atthe azimuthal extremes interacting with portions of the autonomousplatform. The narrow-band RADAR systems can be positioned to cover anangular range of the environment proximate to the autonomous platformthat is sensitive to interference from energy at the azimuthal extremes.This can include, for example, the side of a trailer attached to anautonomous tractor of an autonomous truck.

FIG. 4 is an example autonomous truck 400 according to someimplementations of the present disclosure. Generally, an autonomoustruck can include a tractor unit (e.g., an autonomous tractor) coupledto a trailer. As used herein, an “autonomous truck” can refer to anysuitable autonomous vehicle, including an autonomous tractor coupled toa trailer, an autonomous tractor not coupled to a trailer, an autonomousvehicle incorporating a trailer, an autonomous vehicle towing a trailer,and/or any other suitable autonomous vehicle.

Autonomous truck 400 can be configured to tow trailer 450. Theautonomous truck 400 is one example autonomous platform that can supporta first RADAR sensor 460 and a second RADAR sensor 470 as describedherein. The autonomous truck 400 can navigate along road surface 410. Inparticular, the autonomous truck 400 can include an autonomous vehiclecontrol system that provides for autonomous functionality such as, forexample, perceiving an environment of the autonomous truck 400,determining a trajectory for autonomous truck 400 to successfullynavigate the environment, and/or controlling the autonomous truck 400 toimplement the trajectory. The autonomous truck 400 can includecomponents such as wheels 412, bumper 414, headlight(s) 416, mirrors418, and/or cabin 420.

Additionally, the autonomous truck 400 can include sensor bed 430.Sensor bed 430 can act as a platform for various sensors that providefor autonomous functionality of autonomous truck 400. For instance,sensor bed 430 can include starboard sensors 432, center sensors 434,and/or port sensors 436. Each of the starboard sensors 432, centersensors 434, and/or port sensors 436 can include one or more sensors,including RADAR sensors (e.g., first and/or second RADAR sensors, asdescribed herein), LIDAR sensors, cameras, and/or any other suitablesensors. According to some example implementations of the presentdisclosure, second RADAR sensors can be disposed on edges of the sensorbed 430 such that the sensors have a suitable view of areas proximate tothe trailer 450.

In the example autonomous truck 400, the first RADAR sensor 460 isdisposed near center sensors 434 and the second RADAR sensor 470 isdisposed near port sensors 436. However, the first RADAR sensor 460and/or the second RADAR sensor 470 can be disposed on any suitablelocation of the autonomous truck 400. For instance, when the autonomoustruck 400 includes a sensor bed (e.g., sensor bed 430 positioned abovethe cabin 420 of the autonomous truck 400), the first RADAR sensor 460and/or the second RADAR sensor 470 can be positioned on or within thesensor bed 430. For instance, in some implementations, the first RADARsensor 460 is positioned near a center of the sensor bed 430.Additionally and/or alternatively, the first RADAR sensor 460, or otherfirst RADAR sensors having similar characteristics (e.g., MIMO antennas)can be positioned near edges of the sensor bed 430. Additionally and/oralternatively, the second RADAR sensor 470 can be positioned on an edgeof the sensor bed 430. Second RADAR sensors 470 can be positioned ononly one edge (e.g., an edge corresponding to a direction of higherspeed or shoulder merging, such as near port sensors 456) or both edges.In some implementations, the second RADAR sensors 470 may be positionedon or near a rear portion of the autonomous truck, such as rear edges425 of an autonomous truck such that the sensors 470 are proximate tothe sidewalls of the trailer 450.

As used herein, a “rear” or “rear portion” of an autonomous vehiclerefers to a portion of the vehicle that may be understood to be a rearof the vehicle by any suitable understanding, such as, for example, aportion of the vehicle that is generally oriented opposite to adirection of travel of the vehicle during normal operation, a portion ofthe vehicle including brake lights, tailpipes, a trunk, a rearwindshield, reverse lights, tow hitches, etc., a portion of the vehicleopposite an orientation of seats in the vehicle, or any other suitableunderstanding.

FIGS. 5A-5B are an example operating environment for an autonomousplatform according to some implementations of the present disclosure. Inparticular, FIG. 5A depicts an example operating environment 500 of anautonomous platform 502. FIG. 5A depicts autonomous platform 502 as anautonomous truck towing a trailer. However, it should be understood thatautonomous platform 502 could be any suitable autonomous platform, suchas an autonomous vehicle, a human-driven vehicle with driver assistancefeatures (e.g., blind spot warning indicators, etc.), and/or any othersuitable platform or vehicle. The operating environment 500 canadditionally include one or more actors 504. As illustrated in FIG. 5A,the one or more actors 504 can include one or more vehicles, such asautonomous and/or manually driven vehicles. Other types of actors, suchas pedestrians, stationary objects, traffic control markings (e.g.,signage, stoplights, etc.) can also be included in operating environment500.

FIG. 5B depicts a birds-eye-view sensor data representation 550 ofoperating environment 500 of FIG. 5A. For instance, sensor datarepresentation 550 can depict how the autonomous platform 502 perceivesthe operating environment 500. The representation 550 can includeautonomous platform 552. The autonomous platform 552, corresponding tothe autonomous platform 502 of FIG. 5A, represents a point of referencein two-dimensional or three-dimensional space. For instance, RADARsystems onboard autonomous platform 552 can output a plurality of RADARdata points corresponding to detected objects in operating environment500. For example, RADAR points 556 can correspond to a trailer ofautonomous platform 552. Furthermore, a perception system can outputbounding boxes 554 associated with the actors 504.

Sensor data representation 550 also includes a multipath data point 560.The multipath data point 560 is not associated with a physical actor,but rather caused by “bright” RADAR returns from sidewalls of a trailertowed by autonomous platform 552. Intuitively, the RADAR system is“blinded” to less reflective distance objects due to the trailerreflecting energy from azimuthal extremes of the RADAR beam emitted bythe RADAR system. The multipath data point 560 can present challengesfor object recognition, as it may be falsely recognized as an objectand/or may require additional computing resources (e.g., filtering) toproperly manage. According to example aspects of the present disclosure,multipath can be reduced and/or eliminated by forming a narrow field ofview RADAR beam (e.g., a “pencil beam”) having limited azimuthal extremeenergy, such as less azimuthal extreme energy than that of a MIMOantenna.

FIGS. 6A and 6B are example transmit (and/or receive) patterns for RADARsystems according to some implementations of the present disclosure. Inparticular, FIG. 6A depicts a transmit pattern 600 associated with aMIMO RADAR antenna (e.g., a first antenna) according to exampleimplementations of the present disclosure. The MIMO RADAR antenna canemit a RADAR beam that is generally represented by the transmit pattern600. Transmit pattern 600 can include boresight lobe 605 in boresightdirection 610 and/or one or more azimuthal extremes 615 along azimuthaldirection 620. As used herein, an azimuthal component can refer to anysuitable portion of the transmit pattern 600 along azimuthal direction620 such as, for example, the distance between the azimuthal extremes615, the distance between the azimuthal extremes 615 and a center (e.g.,an azimuthal center) of transmit pattern 600, or other suitablecomponent. As an example, in some embodiments, the transmit pattern 600can include power radiated over a first (e.g., wide) azimuthal componentwith greater than 120 degree azimuthal span and less than 180 degreeazimuthal span. The transmit pattern 600 is emitted by one example of afirst antenna according to example aspects of the present disclosure.

Similarly, FIG. 6B depicts a transmit (or receive) pattern associatedwith a beam steering RADAR antenna (e.g., a second antenna) according toexample implementations of the present disclosure. The beam steeringRADAR antenna can emit a RADAR beam that is generally represented by thetransmit pattern 650. Transmit pattern 650 can include boresight lobe655 in boresight direction 660 and/or one or more azimuthal extremes 665along azimuthal direction 670. As used herein, an azimuthal componentcan refer to any suitable portion of the transmit pattern 650 alongazimuthal direction 670 such as, for example, the distance between theazimuthal extremes 665, the distance between the azimuthal extremes 665and a center (e.g., an azimuthal center) of transmit pattern 650, orother suitable component. As an example, the transmit pattern 650 caninclude power focused in a second (e.g., narrow) azimuthal componentwith less than 1 degree azimuthal span and greater than 0.1 degreeazimuthal span. The transmit pattern 650 is emitted by one example of asecond antenna according to example aspects of the present disclosure.

According to example aspects of the present disclosure, the second RADARbeam can have a narrower azimuthal component than the first RADAR beam.Using the transmit patterns depicted in FIGS. 6A-6B as examples, FIG. 6Amay depict a pattern for the first RADAR beam and FIG. 6B may depict apattern for the second RADAR beam. As illustrated, the azimuthalcomponent 670 of transmit pattern 650 is significantly narrower than theazimuthal component 620 of transmit pattern 600. Additionally, thetransmit pattern 650 covers a greater distance in the boresightdirection 660 than the transmit pattern 600 covers in its boresightdirection 610. Because of this, the second antenna can detect objects ata longer range, but must be swept at a finer resolution compared to thefirst antenna. This can prevent the second antenna from being sufficientfor some object detection applications, as the finer resolution that isrequired can contribute to a slower scanning speed. This, in turn, mayprovide an insufficient latency for object detection applications.However, the second antenna is less sensitive to multipath interference,making it desirable for angular regions that are sensitive to multipathinterference.

The example patterns of FIGS. 6A and 6B are described as transmitpatterns for the purposes of illustration. It should be understood thatthe patterns depicted in FIGS. 6A and 6B may also be referred to as“radiation patterns,” “receive patterns,” or similar, and, in someimplementations, may be used to transmit and/or receive signals atexample antennae.

FIGS. 7A and 7B are example antennas for RADAR systems illustrating beamsteering according to some implementations of the present disclosure.RADAR system 700 is a MIMO antenna configured to output a first beamhaving a first azimuthal component (e.g., a wide azimuthal component).As illustrated in FIG. 7A, subsequent beams from RADAR system 700 can beemitted with a same orientation relative to the antenna. For instance, afirst RADAR beam 712 emitted by RADAR system 700 at a first time canhave a first boresight direction. A subsequent RADAR beam 714 emitted byRADAR system 700 at a second time (e.g., at a subsequent time) can havea second boresight direction that is equal to or about equal to thefirst boresight direction. Additionally and/or alternatively, one ormore azimuthal extremes of the first RADAR beam 712 can be equal to orabout equal to one or more azimuthal extremes of the subsequent RADARbeam 714. For instance, the azimuthal extremes can have equivalentand/or about equivalent intensity and/or positioning.

FIG. 7A illustrates an example beam steering RADAR system 720 accordingto example implementations of the present disclosure. RADAR system 720is a beam steering RADAR system configured to output a second beam 722having a second azimuthal component (e.g., a narrow azimuthalcomponent). For instance, as illustrated in FIG. 7A, the second beam 722can have a significantly narrower azimuthal component than first beam712, which can contribute to reduced multipath interference and/orlonger detection distance, as described herein. Furthermore, asillustrated in FIG. 7A, subsequent beams from RADAR system 720 can havedifferent orientations such that the beams are swept over an angularrange. For instance, a second RADAR beam 722 emitted by RADAR system 720at a first time can have a first boresight direction. A subsequent RADARbeam 724 emitted by RADAR system 720 at a second time (e.g., at asubsequent time) can have a second boresight direction that is equal toor about equal to the first boresight direction. Additionally and/oralternatively, one or more azimuthal extremes of the second RADAR beam722 can be equal to or about equal to one or more azimuthal extremes ofthe subsequent RADAR beam 724. For instance, the azimuthal extremes canhave equivalent and/or about equivalent intensity and/or positioning.

FIG. 7B illustrates a comparison of transmit patterns. First transmitpattern 750 can be emitted by a first antenna, such as a MIMO antenna.Second transmit pattern(s) 760 can be emitted by a second antenna, suchas a beam steering antenna. The first transmit pattern 750 can cover awider (e.g., greater) angular range than the second transmit pattern(s)760. For instance, transmit pattern 750 can cover a same angular rangeas multiple second transmit pattern(s) 760, such as three to four secondtransmit pattern(s) 760, for example. However, second transmitpattern(s) 760 can have a greater gain in the boresight direction thanfirst transmit pattern 750, providing for the second transmit pattern(s)to cover a greater boresight distance in the boresight direction.

For instance, one example aspect of the present disclosure is directedto a RADAR sensor system. The RADAR sensor system can be included in avehicle. The vehicle can be an autonomous vehicle, such as an autonomousvehicle control system configured to control an autonomous vehicle.Additionally and/or alternatively, the vehicle can be a nonautonomousvehicle with RADAR-augmented features, such as blind spot indicators,lane change assistance, automatic braking, and/or other suitablefeatures. In some implementations, the vehicle (e.g., autonomousvehicle) can be coupled to and/or configured to tow a trailer. Forinstance, in some implementations, the vehicle can be or can include anautonomous truck. As one example, in some implementations, at least aportion of the RADAR sensor system (e.g., the at least the RADARsensor(s)) can be incorporated into a sensor bed disposed on an exteriorsurface of the vehicle (e.g., autonomous vehicle). The RADAR sensorsystem can be disposed on any suitable (e.g., exterior) surface of thevehicle. Example aspects of the present disclosure discussed herein withreference to an autonomous vehicle for the purposes of illustration canbe similarly applied to any suitable manually driven vehicles whereappropriate (e.g., vehicles with driver assistance features).

FIGS. 8A-8B are example angular ranges covered by RADAR systems in anenvironment of an autonomous platform according to some implementationsof the present disclosure. In particular, FIG. 8A depicts exampleangular ranges covered by RADAR systems, and FIG. 8B depicts in greaterdetail how a second RADAR sensor can sweep a second beam over a secondangular range. Autonomous platform 810 (e.g., autonomous truck) caninclude a first RADAR sensor including one or more first antennas (e.g.,MIMO antenna(s)). The first antenna(s) can emit RADAR beams to generallycover first angular ranges 815. As illustrated, first angular ranges 815are generally directed towards a front of the autonomous platform 810.Each of the first angular ranges may be covered by a same or separatefirst antenna of the first RADAR sensor. Additionally, autonomousplatform 810 can include a second RADAR sensor including one or moresecond antennas (e.g., beam steering antenna(s)). The second antenna(s)can sweep second RADAR beams over second angular ranges 820 to obtaindetections having a longer detection range and/or reduced sensitivity tomultipath interference. For instance, FIG. 8B depicts one example ofsweeping RADAR beams over second angular ranges 820. In particular, at afirst time, a second RADAR sensor can output an initial beam 825.Initial beam 825 can have a first direction and radiationcharacteristics that correspond to a first transmit pattern. With thesecond RADAR sensor outputting initial beam 825, the second RADAR sensorcan obtain RADAR data indicative of detections in a boresight directionof initial beam 825.

At a second time, the second RADAR sensor can output a first subsequentbeam 830. The first subsequent beam 830 can have a second direction thatis different from the first direction. However, other characteristics ofthe first subsequent beam 830, such as azimuthal component and/orboresight component, can be identical or substantially similar toinitial beam 825. For instance, the initial beam 825 can be rotated orsteered to form the first subsequent beam 830.

An angular offset between boresight direction of initial beam 825 andfirst subsequent beam 830 can be based on a resolution of the secondRADAR sensor. For instance, a greater resolution can correspond to asmaller distance between boresight directions of initial beam 825 andfirst subsequent beam 830. With the second RADAR sensor outputting firstsubsequent beam 830, the second RADAR sensor can obtain RADAR dataindicative of detections in a boresight direction of first subsequentbeam 830. Similarly, first subsequent beam 830 can be rotated (e.g., bythe same amount) to form second subsequent beam 835. This process can berepeated until final beam 850. Once the second RADAR sensor has obtainedRADAR data indicative of directions in a boresight direction of finalbeam 850, the sensor has completed one sweep of the angular range 820.In some implementations, the second RADAR sensor can continuously sweepthe angular range 820. For instance, in some implementations, the secondRADAR sensor can sweep the angular range 820 in the same direction foreach pass. In alternative implementations, the second RADAR sensor cansweep the angular range 820 in a first direction (e.g., clockwise) for afirst pass and sweep the angular range 820 in a second direction (e.g.,counterclockwise) for a second pass.

FIGS. 9A, 9B, 9C, and 9D are example uses cases for applications ofautonomous vehicle control systems according to some implementations ofthe present disclosure. For instance, FIG. 9A depicts an example returnfrom shoulder scenario 900. Autonomous platform 902 may execute a pullto shoulder maneuver for any of various reasons, such as, for example,to clear the way for emergency vehicles, to manage an atypicaloperational status, to prevent merging into oncoming traffic, or anyother suitable reason, such that the autonomous platform 902 exits adriving lane 904 to pull onto shoulder 910.

After performing the pull to shoulder maneuver, the autonomous platform902 should be able to return to the driving lane 904 and resume driving.However, the autonomous platform 902 can plan its motion to avoidoncoming traffic, such as vehicle 906, that may occupy driving lane 904.Challenges are associated with returning to driving lane 904 with theautonomous platform. For instance, if the autonomous platform 902 istowing a heavy load that limits its acceleration, it can be difficultfor the autonomous platform 902 to quickly return to a particular speedfor driving lane 904.

Using the technology of the present disclosure, the autonomous platform902 can have a long detection range along driving lane 904, such that itis ensured to have sufficient area to merge. For instance, theautonomous platform 902 can detect vehicle 906 at a distance such that,by the time autonomous platform 902 has completed a return from shouldertrajectory 914 and moved to position 912, the vehicle 906 can beexpected to be at position 916, which gives autonomous platform 902enough room to return to an acceptable speed. In this way, exampleaspects of the present disclosure provide for an increased detectiondistance and/or reduced likelihood of multipath interference, which canimprove the performance of RADAR systems in scenarios including thereturn from shoulder scenario 900. The use of a second RADAR sensoremploying a narrower-band antenna (e.g., a pencil-band antenna) while afirst RADAR sensor covers a larger angular range of the autonomousplatform 902 can provide for the autonomous platform 902 to detectvehicles with greater distance along the driving lane 904, providingimproved understanding of the environment of the autonomous platform 902and improved decision making by motion planning systems, withoutsacrificing the capability of the autonomous platform 902 to detectobjects in regions other than the driving lane 904 (e.g., regionscovered by the first RADAR sensor).

FIG. 9B depicts an example obstructed lane scenario 920. In theobstructed lane scenario 920, an object 926, such as a disabled orstopped vehicle, debris, road damage, or other suitable object preventsautonomous platform 922 from traveling along lane 924. To avoid thestopped object, the autonomous platform 922 can execute a lane changemaneuver 930 to transition into adjacent lane 928. However, if theautonomous platform 922 is unable to complete the lane change maneuver930 quickly enough, the autonomous platform 922 may reduce its speed toavoid the object 926. Thus, in some cases, autonomous platform 922 maythen plan its motion to return to a particular speed from a reducedspeed or even a standstill while merging into adjacent lane 928. It canthus be beneficial for autonomous platform 922 to have a detection rangeinto adjacent lane 928 that is sufficient to ensure that the autonomousplatform 922 has sufficient area to return to adjacent speed and avoidoncoming traffic, such as vehicle 932. Example aspects of the presentdisclosure provide for an increased detection distance and/or reducedlikelihood of multipath interference, which can improve the performanceof RADAR systems in scenarios including the obstructed lane scenario920. The use of a second RADAR sensor employing a narrower-band antenna(e.g., a pencil-band antenna) while a first RADAR sensor covers a largerangular range of the autonomous platform 922 can provide for theautonomous platform 922 to detect vehicles (e.g., 932) with greaterdistance along the adjacent lane 928, providing improved understandingof the environment of the autonomous platform 922 and improved decisionmaking by motion planning systems, without sacrificing the capability ofthe autonomous platform 922 to detect objects in regions other than theadjacent lane 928 (e.g., regions covered by the first RADAR sensor).

FIG. 9C depicts an example lane closure scenario 940. For instance, lane946 may be closed due to construction or other suitable reason. Trafficcontrol items 950, such as cones, barrels, signage, etc. can instructvehicles from closed lane 946 to merge into adjacent lane 948. Theautonomous platform 942 can execute a lane change maneuver to transitioninto adjacent lane 948. However, if the autonomous platform 942 isunable to complete the lane change maneuver quickly enough, theautonomous platform 942 can reduce its speed to avoid traffic controlitems 950. Thus, in some cases, the autonomous platform 942 may thenreturn to a particular speed from a reduced speed or even a standstillwhile merging into adjacent lane 948. It can thus be beneficial forautonomous platform 942 to have a detection range into adjacent lane 948that is sufficient to ensure that the autonomous platform 942 can returnto adjacent lane 948 with enough speed and avoid oncoming traffic 944.It can be especially desirable in lane closure scenario 940 to provideaccurate detections, due to the increased likelihood of debris, roaddamage, pedestrians (e.g., workers, emergency services, etc.), and otherhigh-interest objects. Example aspects of the present disclosure providefor an increased detection distance and/or reduced likelihood ofmultipath interference, which can improve the performance of RADARsystems in scenarios including the lane closure scenario 940. The use ofa second RADAR sensor employing a narrower-band antenna (e.g., apencil-band antenna) while a first RADAR sensor covers a larger angularrange of the autonomous platform 942 can provide for the autonomousplatform 942 to detect vehicles (e.g., 944) with greater distance alongthe adjacent lane 948, providing improved understanding of theenvironment of the autonomous platform 942 and improved decision makingby motion planning systems, without sacrificing the capability of theautonomous platform 942 to detect objects in regions other than theadjacent lane 948 (e.g., regions covered by the first RADAR sensor).

FIG. 9D depicts an example merge scenario 960. Autonomous platform 962can desirably merge from access lane 964 into driving lane 966 withoutentering shoulder 968. However, stop sign 970 may indicate that theautonomous platform 962 should desirably to a complete standstill beforemerging into driving lane 966. Thus, autonomous platform 962 canaccelerate to a particular speed from a standstill while merging intodriving lane 966. If autonomous platform 962 has limited accelerationdue to, for example, a heavy load, the autonomous platform 962consequently desires a large detection range in the direction of drivinglane 966 to avoid collisions with oncoming traffic, such as vehicle 972.Example aspects of the present disclosure provide for an increaseddetection distance and/or reduced likelihood of multipath interference,which can improve the performance of RADAR systems in scenariosincluding the merge scenario 960. The use of a second RADAR sensoremploying a narrower-band antenna (e.g., a pencil-band antenna) while afirst RADAR sensor covers a larger angular range of the autonomousplatform 962 can provide for the autonomous platform 962 to detectvehicles (e.g., 972) with greater distance along the driving lane 966,providing improved understanding of the environment of the autonomousplatform 962 and improved decision making by motion planning systems,without sacrificing the capability of the autonomous platform 962 todetect objects in regions other than the driving lane 966 (e.g., regionscovered by the first RADAR sensor).

FIG. 10 is a flowchart of a method 1000 according to someimplementations of the present disclosure. One or more portions of themethod 1000 can be implemented by one or more devices (e.g., one or morecomputing devices) or systems including, for example, the computingsystem 180 shown in FIG. 1 , the autonomy system(s) 200 shown in FIG. 2, the computing ecosystem 10 of FIG. 11 , and/or any other suitablesystems or devices. Moreover, one or more portions of the method 1000can be implemented as an algorithm on the hardware components of thedevices described herein. FIG. 10 depicts elements performed in aparticular order for purposes of illustration and discussion. Those ofordinary skill in the art, using the disclosures provided herein, willunderstand that the elements of any of the methods discussed herein canbe adapted, rearranged, expanded, omitted, combined, and/or modified invarious ways without deviating from the scope of the present disclosure.

The method 1000 includes, at 1010, obtaining first RADAR data includingone or more first data points associated with a first angular range ofan environment of an autonomous vehicle from a first RADAR sensor. Thefirst RADAR sensor can include a first antenna configured to output afirst RADAR beam having a first (e.g., wide) azimuthal component. Forinstance, in some implementations, the first antenna can be a MIMOantenna. The MIMO antenna can be configured to output a RADAR beamhaving a generally wider azimuthal component than, for example, a beamsteering antenna. The MIMO antenna can include a plurality of MIMOtransceivers (e.g., transmitters and/or receivers) that collectivelyoperate to receive and/or transmit RADAR signals through an antennaarray. Each transceiver may be disposed on or within a separateintegrated circuit (IC) and/or multiple transceivers may be disposed ona same IC. Multiple transceivers may be disposed on separate or samemodules, boards, cards, housings, or other units.

For instance, a RADAR sensor system can include a first RADAR sensorconfigured to provide first RADAR data descriptive of an environment ofa vehicle. The first RADAR sensor can include a first antenna configuredto output a first RADAR beam having a first azimuthal component. Thefirst RADAR sensor can emit the first RADAR beam according to a firsttransmit pattern including the first azimuthal component and a firstboresight component. For instance, the first transmit pattern can be atwo-dimensional and/or three-dimensional representation of the energyemitted in the first RADAR beam. The first transmit pattern can includeone or more lobes, such as a boresight lobe and/or one or more azimuthalextremes, that mark extrema of the transmit pattern. The first azimuthalcomponent can be based on the one or more azimuthal extremes, such as byan azimuthal distance between one of the azimuthal extremes and a centerof the transmit pattern, between two azimuthal extremes, or by otherreference to the azimuthal extreme(s). The first azimuthal component canbe a wide azimuthal component that is or can be greater than about 30percent of the first boresight component. For instance, an energyintensity at the azimuthal extreme can be greater than about 30 percentof a boresight energy intensity at the boresight lobe and/or less thanabout 100 percent of the boresight energy intensity. For example, if theboresight component has a maximum intensity of 100 dBi (e.g., with aboresight lobe at 100 dBi), the azimuthal component may be greater thanabout 30 dBi (e.g., with azimuthal extreme(s) at 30 or more dBi). As anexample, the first transmit pattern can include power radiated over afirst (e.g., wide) azimuthal component with greater than 120 degreeazimuthal span and less than 180 degree azimuthal span. It should beunderstood that a transmit pattern is a nontransient representation ofenergy emitted by an antenna, so actual RADAR beams emitted by theantenna may not exactly match the transmit pattern at all times, butwill generally behave according to the transmit pattern.

For instance, in some implementations, the first antenna can be a MIMOantenna. The MIMO antenna can be configured to output a RADAR beamhaving a generally wider azimuthal component than, for example, a beamsteering antenna. The MIMO antenna can include a plurality of MIMOtransceivers (e.g., transmitters and/or receivers) that collectivelyoperate to receive and/or transmit RADAR signals through an antennaarray. Each transceiver may be disposed on or within a separateintegrated circuit (IC) and/or multiple transceivers may be disposed ona same IC. Multiple transceivers may be disposed on separate or samemodules, boards, cards, housings, or other units. As used herein,transceivers can refer to devices capable of both transmit and receivefunctions in addition to and/or alternatively to devices capable of onlyone of transmit or receive functions, unless indicated otherwise.

The first RADAR antenna can be configured to output the first RADAR beamover a first angular range. The first angular range can be configured tocover a front portion of the vehicle, such as a front of the vehicle. Asused herein, a “front” or “front portion” of an autonomous vehiclerefers to a portion of the vehicle that may be understood to be a frontof the vehicle by any suitable understanding, such as, for example, aportion of the vehicle that is generally oriented toward a direction oftravel of the vehicle during normal operation, a portion of the vehicleincluding an engine block, windshield, grill, bumper, headlights, etc.,a portion of the vehicle towards which seats in the vehicle face, or anyother suitable understanding. Additionally and/or alternatively, in someimplementations, the first angular range can cover a majority of thevehicle (e.g., at least 180 degrees surrounding the vehicle). As usedherein, a RADAR sensor can cover an angular range if the sensor isconfigured to transmit and/or receive RADAR signals within the angularrange. For instance, the MIMO antenna of the first RADAR sensor cangenerally cover a wider angular range while still providing desirablefrequency and resolution within the wider range.

The method 1000 includes, at 1020, obtaining second RADAR data includingone or more second data points associated with a second angular range ofthe environment of the autonomous vehicle from a second RADAR sensor.The second RADAR sensor can include a second antenna configured tooutput a second RADAR beam having a second (e.g., narrow) azimuthalcomponent that is narrower than the first (e.g., wide) azimuthalcomponent of the first RADAR beam. For example, the second antenna canbe a beam steering antenna emitting a beam according to a transmitpattern having a second azimuthal component. The second azimuthalcomponent can be narrower than a first azimuthal component of the firstRADAR sensor such that the second RADAR beam is less affected bymultipath interference from components of an autonomous vehicle, such assidewalls of a trailer. For instance, in some implementations, thesecond azimuthal component can be a narrow azimuthal component that isor can be less than about 30 percent of the second boresight componentand/or greater than about 1 percent of the second boresight component.For instance, in some implementations, the second azimuthal componentcan be a narrow azimuthal component that is or can be greater than about50 percent of the second boresight component. As an example, a transmitpattern of the second antenna can include power focused in a narrowazimuthal component with less than 1 degree azimuthal span and greaterthan 0.1 degree azimuthal span.

The autonomous vehicle can further include a second RADAR sensor using awider-band antenna, such as a multiple-input-multiple-output (MIMO)antenna. The second RADAR sensor can cover other (e.g., larger) angularranges of the environment proximate to the autonomous platform. Forinstance, the narrow profile of the narrow-band RADAR system can beswept over a smaller angular range (e.g., towards the rear of anautonomous vehicle) while the additional RADAR system uses a wider-bandantenna to capture information over the remaining angular range. In thisway, the RADAR systems can provide improved detection of objects in theenvironment having less interference from components of the autonomousplatform. Furthermore, in some implementations, an additional RADARsystem using a wider-band antenna, such as amultiple-input-multiple-output (MIMO) antenna, can cover other (e.g.,larger) angular ranges of the environment proximate to the autonomousplatform. For instance, the narrow profile of the narrow-band RADARsystem can be swept over a smaller angular range (e.g., towards the rearof an autonomous vehicle) while the additional RADAR system uses awider-band antenna to capture information over the remaining angularrange. In this way, the RADAR systems can provide improved detection ofobjects in the environment having less interference from components ofthe autonomous platform.

As used herein, “sweeping” a RADAR beam over an angular range refers toany process, method, or operation by which RADAR data is captured overthe angular range in segmented portions. For instance, a RADAR sensorcapable of generating RADAR data over a certain range less than theangular range to be swept may be positioned at a first direction ororientation such that the RADAR sensor captures RADAR data in the firstdirection. The RADAR sensor may then be reoriented, by rotation,movement, electrical recalibration, or otherwise, such that the RADARsensor is positioned at a second direction or orientation. The RADARsensor may then capture RADAR data in the second direction. This processcan be repeated until the RADAR sensor has observed the entire angularrange to be swept over. The RADAR system may continually scan theangular range, such as by repeating the sweep from the beginning oncethe sweep has completed.

The second antenna can be configured to output a second RADAR beamhaving a second azimuthal component that is narrower than the firstazimuthal component of the first RADAR beam. For instance, the secondRADAR sensor can emit the second RADAR beam according to a secondtransmit pattern including the second azimuthal component and a secondboresight component. For instance, the second transmit pattern can be atwo-dimensional and/or three-dimensional representation of the energyemitted in the second RADAR beam. The second transmit pattern caninclude one or more lobes, such as a boresight lobe and/or one or moreazimuthal extremes, that mark extrema of the transmit pattern. Thesecond azimuthal component can be based on the one or more azimuthalextremes, such as by an azimuthal distance between one of the azimuthalextremes and a center of the transmit pattern, between two azimuthalextremes, or by other reference to the azimuthal extreme(s).

According to example aspects of the present disclosure, the secondazimuthal component can be narrower than the first azimuthal component,such that the second RADAR beam is less affected by multipathinterference from components of an autonomous vehicle, such as sidewallsof a trailer. For instance, in some implementations, the secondazimuthal component can be a narrow azimuthal component that is or canbe less than about 30 percent of the second boresight component and/orgreater than about 1 percent of the second boresight component. Forinstance, an energy intensity at the azimuthal extreme can be less thanabout 30 percent of a boresight energy intensity at the boresight lobeand/or greater than about 1 percent of the boresight energy intensity.For example, if the boresight component has a maximum intensity of 100dBi (e.g., with a boresight lobe at 100 dBi), the azimuthal componentmay be less than about 30 dBi (e.g., with azimuthal extreme(s) at 30 orfewer dBi). Furthermore, as another example, if the first RADAR antennais a MIMO antenna emitting a first RADAR beam having a azimuthal extremeintensity of about 40 dBi, the second RADAR antenna can be a beamsteering antenna configured to form a “pencil beam” having a azimuthalextreme intensity of about 10 dBi.

Although the use of a beam steering antenna can provide for highdetection range and reduced multipath interference, the angular rangecovered by each emitted beam can be limited. For instance, some beamsteering antennas may only provide reliable detections at each emittedbeam with a resolution that is a fraction of a degree. Thus, coveringbroad angular ranges with a narrow beam steering antenna can presentlatency challenges associated with sweeping the beam over the broadangular range.

The first RADAR sensor and/or the second RADAR system can be included inand/or otherwise coupled to an autonomous vehicle control system. Theautonomous vehicle control system can be configured to control theautonomous vehicle (and/or other types of autonomous platform(s)). Forinstance, the autonomous vehicle control system can include one or moreprocessors and one or more non-transitory, computer-readable mediastoring instructions that are executable to cause the one or moreprocessors to perform operations for implementing the methods,processes, and other steps described herein.

Additionally, the second RADAR sensor can be configured to sweep thesecond RADAR beam over a second angular range to obtain the second RADARdata. The second angular range can be closer to a rear of the autonomousvehicle than a front of the vehicle. As an example, if a front of thevehicle is used as a zero-degree point of reference, the second angularrange may include angular values not exceeding 90 degrees to about 270degrees. For instance, the second antenna can be directed to a trailercoupled to the vehicle and/or proximate to the trailer coupled to thevehicle. For instance, the second antenna can be or can include a beamsteering antenna that is directed to a trailer coupled to the vehicle.For instance, the second angular range can be configured to be proximateto (e.g., bordering) a trailer coupled to the vehicle. The secondangular range can be a limited range, such as a range having a maximumspan of about 30 degrees and/or a minimum span of about 1 degree.

In some implementations, obtaining the second RADAR data can includesweeping the second RADAR beam over a second angular range. Forinstance, the second RADAR sensor can be configured to sweep the secondRADAR beam over the second angular range which is closer to a rear ofthe autonomous vehicle than a front of the autonomous vehicle to obtainthe second RADAR data. In some implementations, sweeping the secondRADAR beam over the second angular range can include broadcasting thesecond RADAR beam in a first angular direction of the second angularrange. For instance, the second RADAR beam can be emitted with theboresight direction of the second RADAR beam directed in the firstangular direction. The first angular direction can fall within and/orborder the second angular range. The second RADAR sensor can then obtaina first portion of the second RADAR data associated with the firstangular direction with the second RADAR beam broadcasted in the firstangular direction. For instance, the first portion of the second RADARdata can include detections of objects in the first angular directionand/or closely bordering the first angular direction. The second RADARsensor can then broadcast the second RADAR beam in a second angulardirection of the second angular range. For instance, the second RADARbeam can be shifted or rotated such that the boresight direction isaligned with the second angular direction. The second RADAR sensor canthen obtain a second portion of the second RADAR data associated withthe second angular direction with the second RADAR beam broadcasted inthe second angular direction. For instance, the second portion of thesecond RADAR data can include detections of objects in the secondangular direction and/or closely bordering the second angular direction.This process can be repeated over several iterations until the entiresecond angular range is covered, with a desired resolution, and/orrestarted once each sweep of the second angular range is complete.

The method 1000 includes, at 1030, detecting one or more objects in theenvironment of the autonomous vehicle based on the first RADAR data andthe second RADAR data. For instance, detecting one or more objects inthe environment of the autonomous vehicle based on the first RADAR dataand the second RADAR data can include providing the first RADAR data andthe second RADAR data to a perception system of the autonomous vehicle.In some implementations, providing the first RADAR data and the secondRADAR data to a perception system can include providing the first RADARdata and the second RADAR data to a sensor data fusion module configuredto fuse at least the first RADAR data and the second RADAR data togenerate fused RADAR data including a point-cloud representation of theenvironment of the autonomous vehicle.

Additionally and/or alternatively, the method 1000 can include, at 1040,determining, based on the one or more objects in the environment of theautonomous platform, a motion trajectory for navigating the autonomousplatform. For instance, the objects detected by the perception systemcan be provided to a planning system configured to output the motiontrajectory to navigate through the environment of the autonomousplatform with respect to objects, traffic regulations, and other roadconsiderations.

The method 1000 can include, at 1050, controlling the autonomousplatform based on the motion trajectory to navigate the autonomousplatform through the environment. For instance, control systems onboardthe autonomous platform, such as steering systems, braking systems,indicator systems, lights, or other control system can be operated inaccordance with the motion trajectory to control the autonomous platformand execute the motion trajectory. The autonomous platform can thus benavigated through its environment.

FIG. 11 is a block diagram of an example computing ecosystem 10according to some implementations of the present disclosure. The examplecomputing ecosystem 10 can include a first computing system 20 and asecond computing system 40 that are communicatively coupled over one ormore networks 60. In some implementations, the first computing system 20or the second computing 40 can implement one or more of the systems,operations, or functionalities described herein (e.g., the remotesystem(s) 160, the onboard computing system(s) 180, the autonomysystem(s) 200, etc.).

In some implementations, the first computing system 20 can be includedin an autonomous platform and be utilized to perform the functions of anautonomous platform as described herein. For example, the firstcomputing system 20 can be located onboard an autonomous vehicle andimplement autonomy system(s) for autonomously operating the autonomousvehicle. In some implementations, the first computing system 20 canrepresent the entire onboard computing system or a portion thereof(e.g., the localization system 230, the perception system 240, theplanning system 250, the control system 260, or a combination thereof,etc.). In other implementations, the first computing system 20 may notbe located onboard an autonomous platform. The first computing system 20can include one or more distinct physical computing devices 21.

The first computing system 20 (e.g., the computing device(s) 21 thereof)can include one or more processors 22 and a memory 23. The one or moreprocessors 22 can be any suitable processing device (e.g., a processorcore, a microprocessor, an ASIC, a FPGA, a controller, amicrocontroller, etc.) and can be one processor or a plurality ofprocessors that are operatively connected. The memory 23 can include oneor more non-transitory computer-readable storage media, such as RAM,ROM, EEPROM, EPROM, one or more memory devices, flash memory devices,etc., and combinations thereof.

The memory 23 can store information that can be accessed by the one ormore processors 22. For instance, the memory 23 (e.g., one or morenon-transitory computer-readable storage media, memory devices, etc.)can store data 24 that can be obtained (e.g., received, accessed,written, manipulated, created, generated, stored, pulled, downloaded,etc.). The data 24 can include, for instance, sensor data (e.g., RADARdata), map data, data associated with autonomy functions (e.g., dataassociated with the perception, planning, or control functions),simulation data, or any data or information described herein. As oneexample, the data 24 can include first RADAR data obtained from a firstRADAR sensor including a first antenna configured to output a firstRADAR beam having a first azimuthal component over a first angularrange. Additionally and/or alternatively, the data 24 can include secondRADAR data obtained from a second RADAR sensor including a secondantenna configured to output a second RADAR beam having a secondazimuthal component that is narrower than the first azimuthal componentof the first RADAR beam. In some implementations, the first computingsystem 20 can obtain data from one or more memory device(s) that areremote from the first computing system 20.

The memory 23 can store computer-readable instructions 25 that can beexecuted by the one or more processors 22. The instructions 25 can besoftware written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 25 can be executed in logically or virtually separatethreads on the processor(s) 22.

For example, the memory 23 can store instructions 25 that are executableby one or more processors (e.g., by the one or more processors 22, byone or more other processors, etc.) to perform (e.g., with the computingdevice(s) 21, the first computing system 20, or other system(s) havingprocessors executing the instructions) any of the operations, functions,or methods/processes (or portions thereof) described herein.

In some implementations, the first computing system 20 can store orinclude one or more models 26. In some implementations, the models 26can be or can otherwise include one or more machine-learned models. Asexamples, the models 26 can be or can otherwise include variousmachine-learned models such as, for example, regression networks,generative adversarial networks, neural networks (e.g., deep neuralnetworks), support vector machines, decision trees, ensemble models,k-nearest neighbors models, Bayesian networks, or other types of modelsincluding linear models or non-linear models. Example neural networksinclude feed-forward neural networks, recurrent neural networks (e.g.,long short-term memory recurrent neural networks), convolutional neuralnetworks, or other forms of neural networks. For example, the firstcomputing system 20 can include one or more models for implementingsubsystems of the autonomy system(s) 200, including any of: thelocalization system 230, the perception system 240, the planning system250, or the control system 260.

In some implementations, the first computing system 20 can obtain theone or more models 26 using communication interface(s) 27 to communicatewith the second computing system 40 over the network(s) 60. Forinstance, the first computing system 20 can store the model(s) 26 (e.g.,one or more machine-learned models) in the memory 23. The firstcomputing system 20 can then use or otherwise implement the models 26(e.g., by the processors 22). By way of example, the first computingsystem 20 can implement the model(s) 26 to localize an autonomousplatform in an environment, perceive an autonomous platform'senvironment or objects therein, plan one or more future states of anautonomous platform for moving through an environment, control anautonomous platform for interacting with an environment, etc.

The second computing system 40 can include one or more computing devices41. The second computing system 40 can include one or more processors 42and a memory 43. The one or more processors 42 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 43can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flashmemory devices, etc., and combinations thereof.

The memory 43 can store information that can be accessed by the one ormore processors 42. For instance, the memory 43 (e.g., one or morenon-transitory computer-readable storage media, memory devices, etc.)can store data 44 that can be obtained. The data 44 can include, forinstance, sensor data, model parameters, map data, simulation data,simulated environmental scenes, simulated sensor data, data associatedwith vehicle trips/services, or any data or information describedherein. As one example, the data 44 can include first RADAR dataobtained from a first RADAR sensor including a first antenna configuredto output a first RADAR beam having a first azimuthal component over afirst angular range. Additionally and/or alternatively, the data 44 caninclude second RADAR data obtained from a second RADAR sensor includinga second antenna configured to output a second RADAR beam having asecond azimuthal component that is narrower than the first azimuthalcomponent of the first RADAR beam. In some implementations, the secondcomputing system 40 can obtain data from one or more memory device(s)that are remote from the second computing system 40.

The memory 43 can also store computer-readable instructions 45 that canbe executed by the one or more processors 42. The instructions 45 can besoftware written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 45 can be executed in logically or virtually separatethreads on the processor(s) 42.

For example, the memory 43 can store instructions 45 that are executable(e.g., by the one or more processors 42, by the one or more processors22, by one or more other processors, etc.) to perform (e.g., with thecomputing device(s) 41, the second computing system 40, or othersystem(s) having processors for executing the instructions, such ascomputing device(s) 21 or the first computing system 20) any of theoperations, functions, or methods/processes described herein. This caninclude, for example, the functionality of the autonomy system(s) 200(e.g., localization, perception, planning, control, etc.) or otherfunctionality associated with an autonomous platform (e.g., remoteassistance, mapping, fleet management, trip/service assignment andmatching, etc.).

In some implementations, the second computing system 40 can include oneor more server computing devices. In the event that the second computingsystem 40 includes multiple server computing devices, such servercomputing devices can operate according to various computingarchitectures, including, for example, sequential computingarchitectures, parallel computing architectures, or some combinationthereof.

In addition, or alternatively to, the model(s) 26 at the first computingsystem 20, the second computing system 40 can include one or more models46. As examples, the model(s) 46 can be or can otherwise include variousmachine-learned models such as, for example, regression networks,generative adversarial networks, neural networks (e.g., deep neuralnetworks), support vector machines, decision trees, ensemble models,k-nearest neighbors models, Bayesian networks, or other types of modelsincluding linear models or non-linear models. Example neural networksinclude feed-forward neural networks, recurrent neural networks (e.g.,long short-term memory recurrent neural networks), convolutional neuralnetworks, or other forms of neural networks. For example, the secondcomputing system 40 can include one or more models of the autonomysystem(s) 200.

In some implementations, the second computing system 40 or the firstcomputing system 20 can train one or more machine-learned models of themodel(s) 26 or the model(s) 46 through the use of one or more modeltrainers 47 and training data 48. The model trainer(s) 47 can train anyone of the model(s) 26 or the model(s) 46 using one or more training orlearning algorithms. One example training technique is backwardspropagation of errors. In some implementations, the model trainer(s) 47can perform supervised training techniques using labeled training data.In other implementations, the model trainer(s) 47 can performunsupervised training techniques using unlabeled training data. In someimplementations, the training data 48 can include simulated trainingdata (e.g., training data obtained from simulated scenarios, inputs,configurations, environments, etc.). In some implementations, the secondcomputing system 40 can implement simulations for obtaining the trainingdata 48 or for implementing the model trainer(s) 47 for training ortesting the model(s) 26 or the model(s) 46. By way of example, the modeltrainer(s) 47 can train one or more components of a machine-learnedmodel for the autonomy system(s) 200 through unsupervised trainingtechniques using an objective function (e.g., costs, rewards,heuristics, constraints, etc.). In some implementations, the modeltrainer(s) 47 can perform a number of generalization techniques toimprove the generalization capability of the model(s) being trained.Generalization techniques include weight decays, dropouts, or othertechniques.

The first computing system 20 and the second computing system 40 caneach include communication interfaces 27 and 49, respectively. Thecommunication interfaces 27, 49 can be used to communicate with eachother or one or more other systems or devices, including systems ordevices that are remotely located from the first computing system 20 orthe second computing system 40. The communication interfaces 27, 49 caninclude any circuits, components, software, etc. for communicating withone or more networks (e.g., the network(s) 60). In some implementations,the communication interfaces 27, 49 can include, for example, one ormore of a communications controller, receiver, transceiver, transmitter,port, conductors, software or hardware for communicating data.

The network(s) 60 can be any type of network or combination of networksthat allows for communication between devices. In some embodiments, thenetwork(s) can include one or more of a local area network, wide areanetwork, the Internet, secure network, cellular network, mesh network,peer-to-peer communication link or some combination thereof and caninclude any number of wired or wireless links. Communication over thenetwork(s) 60 can be accomplished, for instance, through a networkinterface using any type of protocol, protection scheme, encoding,format, packaging, etc.

FIG. 11 illustrates one example computing ecosystem 10 that can be usedto implement the present disclosure. Other systems can be used as well.For example, in some implementations, the first computing system 20 caninclude the model trainer(s) 47 and the training data 48. In suchimplementations, the model(s) 26, 46 can be both trained and usedlocally at the first computing system 20. As another example, in someimplementations, the computing system 20 may not be connected to othercomputing systems. In addition, components illustrated or discussed asbeing included in one of the computing systems 20 or 40 can instead beincluded in another one of the computing systems 20 or 40.

Computing tasks discussed herein as being performed at computingdevice(s) remote from the autonomous platform (e.g., autonomous vehicle)can instead be performed at the autonomous platform (e.g., via a vehiclecomputing system of the autonomous vehicle), or vice versa. Suchconfigurations can be implemented without deviating from the scope ofthe present disclosure. The use of computer-based systems allows for agreat variety of possible configurations, combinations, and divisions oftasks and functionality between and among components.Computer-implemented operations can be performed on a single componentor across multiple components. Computer-implemented tasks or operationscan be performed sequentially or in parallel. Data and instructions canbe stored in a single memory device or across multiple memory devices.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, orvariations within the scope and spirit of the appended claims can occurto persons of ordinary skill in the art from a review of thisdisclosure. Any and all features in the following claims can be combinedor rearranged in any way possible. Accordingly, the scope of the presentdisclosure is by way of example rather than by way of limitation, andthe subject disclosure does not preclude inclusion of suchmodifications, variations or additions to the present subject matter aswould be readily apparent to one of ordinary skill in the art. Moreover,terms are described herein using lists of example elements joined byconjunctions such as “and,” “or,” “but,” etc. It should be understoodthat such conjunctions are provided for explanatory purposes only. Listsjoined by a particular conjunction such as “or,” for example, can referto “at least one of” or “any combination of” example elements listedtherein, with “or” being understood as “and/or” unless otherwiseindicated. Also, terms such as “based on” should be understood as “basedon.”

Those of ordinary skill in the art, using the disclosures providedherein, will understand that the elements of any of the claims,operations, or processes discussed herein can be adapted, rearranged,expanded, omitted, combined, or modified in various ways withoutdeviating from the scope of the present disclosure. Some of the claimsare described with a letter reference to a claim element for exemplaryillustrated purposes and is not meant to be limiting. The letterreferences do not imply a particular order of operations. For instance,letter identifiers such as (a), (b), (c), . . . , (i), (ii), (iii), . .. , etc. can be used to illustrate operations. Such identifiers areprovided for the ease of the reader and do not denote a particular orderof steps or operations. An operation illustrated by a list identifier of(a), (i), etc. can be performed before, after, or in parallel withanother operation illustrated by a list identifier of (b), (ii), etc.

What is claimed is:
 1. A radio detection and ranging (RADAR) sensorsystem for an autonomous truck, the RADAR sensor system comprising: (a)a first RADAR sensor configured to generate first RADAR data descriptiveof an environment of the autonomous truck, the first RADAR sensorcomprising a first antenna configured to output a first RADAR beamhaving a first azimuthal component over a first angular range; and (b) asecond RADAR sensor configured to provide second RADAR data descriptiveof the environment of the autonomous truck, the second RADAR sensorcomprising a second antenna configured to output a second RADAR beamhaving a second azimuthal component that is narrower than the firstazimuthal component of the first RADAR beam, wherein the second RADARsensor is configured to sweep the second RADAR beam over a secondangular range closer to a rear of the autonomous truck than a front ofthe autonomous truck to obtain the second RADAR data, the second angularrange configured to be proximate to a trailer coupled to the autonomoustruck; wherein a transmit pattern of the second antenna comprises powerfocused in a smaller azimuthal component than a transmit pattern of thefirst antenna.
 2. The RADAR sensor system of claim 1, wherein the secondantenna comprises a beam steering antenna.
 3. The RADAR sensor system ofclaim 1, wherein the first angular range is configured to cover thefront of the autonomous truck.
 4. The RADAR sensor system of claim 1,wherein a transmit pattern of the second antenna comprises power focusedin the second azimuthal component with less than 1 degree azimuthal spanand greater than 0.1 degree azimuthal span.
 5. The RADAR sensor systemof claim 1, wherein a transmit pattern of the first antenna comprisespower radiated over the first azimuthal component with greater than 120degree azimuthal span and less than 180 degree azimuthal span.
 6. TheRADAR sensor system of claim 1, wherein the second angular rangecomprises less than thirty degrees and greater than zero degrees.
 7. Anautonomous vehicle control system comprising: (a) one or moreprocessors; and (b) one or more non-transitory, computer-readable mediastoring instructions that are executable to cause the one or moreprocessors to perform operations comprising: (i) obtaining first RADARdata comprising one or more first data points associated with a firstangular range of an environment of an autonomous truck from a firstRADAR sensor, wherein the first RADAR sensor comprises a first antennaconfigured to output a first RADAR beam having a first azimuthalcomponent; (ii) obtaining second RADAR data comprising one or moresecond data points associated with a second angular range of theenvironment of the autonomous truck from a second RADAR sensor, whereinobtaining the second RADAR data comprises sweeping a second RADAR beamover a second angular range, wherein the second RADAR sensor comprises asecond antenna configured to output the second RADAR beam having asecond azimuthal component that is narrower than the first azimuthalcomponent of the first RADAR beam, wherein the second RADAR sensor isconfigured to sweep the second RADAR beam over the second angular rangewhich is closer to a rear of the autonomous truck than a front of theautonomous truck to obtain the second RADAR data, the second angularrange configured to be proximate to a trailer coupled to the autonomoustruck; wherein a transmit pattern of the second antenna comprises powerfocused in a smaller azimuthal component than a transmit pattern of thefirst antenna; and (iii) detecting one or more objects in theenvironment of the autonomous truck based on the first RADAR data andthe second RADAR data.
 8. The autonomous vehicle control system of claim7, wherein detecting the one or more objects in the environment of theautonomous truck based on the first RADAR data and the second RADAR datacomprises: providing the first RADAR data and the second RADAR data to aperception system of the autonomous truck.
 9. The autonomous vehiclecontrol system of claim 8, wherein providing the first RADAR data andthe second RADAR data to the perception system comprises providing thefirst RADAR data and the second RADAR data to a sensor data fusionmodule configured to fuse at least the first RADAR data and the secondRADAR data to generate fused RADAR data comprising a point-cloudrepresentation of the environment of the autonomous truck.
 10. Theautonomous vehicle control system of claim 7, wherein sweeping thesecond RADAR beam over the second angular range comprises: broadcastingthe second RADAR beam in a first angular direction of the second angularrange; obtaining a first portion of the second RADAR data associatedwith the first angular direction with the second RADAR beam broadcastedin the first angular direction; broadcasting the second RADAR beam in asecond angular direction of the second angular range; and obtaining asecond portion of the second RADAR data associated with the secondangular direction with the second RADAR beam broadcasted in the secondangular direction.
 11. The autonomous vehicle control system of claim 7,wherein the operations further comprise: (iv) determining, based on theone or more objects in the environment of the autonomous truck, a motiontrajectory for navigating the autonomous truck; and (v) controlling theautonomous truck based on the motion trajectory to navigate theautonomous truck through the environment.
 12. The autonomous vehiclecontrol system of claim 7, wherein the second RADAR sensor is positionedon a rear position of the autonomous truck.
 13. The autonomous vehiclecontrol system of claim 7, wherein the autonomous truck comprises asensor bed positioned above a cabin of the autonomous truck, and whereinthe second RADAR sensor is positioned within the sensor bed.
 14. Anautonomous truck, comprising: (a) a first RADAR sensor comprising afirst antenna configured to output a first RADAR beam over a firstangular range, the first RADAR beam having a first azimuthal component;(b) a second RADAR sensor comprising a second antenna configured tooutput a second RADAR beam having a second azimuthal component that isnarrower than the first azimuthal component of the first RADAR beam,wherein the second RADAR sensor is configured to sweep the second RADARbeam over a second angular range which is closer to a rear of theautonomous truck than a front of the autonomous truck, the secondangular range configured to be proximate to a trailer coupled to theautonomous truck; wherein a transmit pattern of the second antennacomprises power focused in a smaller azimuthal component than a transmitpattern of the first antenna; and (c) an autonomous vehicle controlsystem comprising one or more processors and one or more non-transitory,computer-readable media storing instructions that are executable tocause the one or more processors to perform operations comprising: (i)obtaining first RADAR data comprising one or more first data pointsassociated with the first angular range of an environment of theautonomous truck from the first RADAR sensor; (ii) obtaining secondRADAR data comprising one or more second data points associated with thesecond angular range of the environment of the autonomous truck from thesecond RADAR sensor, wherein obtaining the second RADAR data comprisessweeping the second RADAR beam over the second angular range; and (iii)detecting one or more objects in the environment of the autonomous truckbased on the first RADAR data and the second RADAR data.
 15. Theautonomous truck of claim 14, wherein the operations further comprise:(iv) determining, based on the one or more objects in the environment ofthe autonomous truck, a motion trajectory for navigating the autonomoustruck; and (v) controlling the autonomous truck based on the motiontrajectory to navigate the autonomous truck through the environment. 16.The autonomous truck of claim 14, wherein sweeping the second RADAR beamover the second angular range comprises: broadcasting the second RADARbeam in a first angular direction of the second angular range; obtaininga first portion of the second RADAR data associated with the firstangular direction with the second RADAR beam broadcasted in the firstangular direction; broadcasting the second RADAR beam in a secondangular direction of the second angular range; and obtaining a secondportion of the second RADAR data associated with the second angulardirection with the second RADAR beam broadcasted in the second angulardirection.
 17. The autonomous truck of claim 14, wherein detecting theone or more objects in the environment of the autonomous truck based onthe first RADAR data and the second RADAR data comprises: providing thefirst RADAR data and the second RADAR data to a perception system of theautonomous truck, wherein providing the first RADAR data and the secondRADAR data to the perception system comprises providing the first RADARdata and the second RADAR data to a sensor data fusion module configuredto fuse at least the first RADAR data and the second RADAR data togenerate fused RADAR data comprising a point-cloud representation of theenvironment of the autonomous truck.